Comparison of Seismicity Models Generated by Different Kernel Estimations

2002 ◽  
Vol 92 (3) ◽  
pp. 913-922 ◽  
Author(s):  
C. Stock
Keyword(s):  
Seismicity Models ◽  
Kernel Estimations

scholarly journals On the testing of seismicity models

2011 ◽  
Vol 60 (3) ◽  
pp. 624-637 ◽  
Author(s):  
George Molchan
Keyword(s):  
Seismicity Models

scholarly journals Pseudo-prospective testing of 5-year earthquake forecasts for California using inlabru

2022 ◽  
Author(s):  
Kirsty Bayliss ◽  
Mark Naylor ◽  
Farnaz Kamranzad ◽  
Ian Main
Keyword(s):  
Fault Location ◽  
Slip Rate ◽  
Process Models ◽  
Earthquake Forecasting ◽  
Earthquake Catalogues ◽  
Slip Rates ◽  
Time Space ◽  
Seismicity Models ◽  
Time Periods ◽  
Grid Based

Abstract. Probabilistic earthquake forecasts estimate the likelihood of future earthquakes within a specified time-space-magnitude window and are important because they inform planning of hazard mitigation activities on different timescales. The spatial component of such forecasts, expressed as seismicity models, generally rely upon some combination of past event locations and underlying factors which might affect spatial intensity, such as strain rate, fault location and slip rate or past seismicity. For the first time, we extend previously reported spatial seismicity models, generated using the open source inlabru package, to time-independent earthquake forecasts using California as a case study. The inlabru approach allows the rapid evaluation of point process models which integrate different spatial datasets. We explore how well various candidate forecasts perform compared to observed activity over three contiguous five year time periods using the same training window for the seismicity data. In each case we compare models constructed from both full and declustered earthquake catalogues. In doing this, we compare the use of synthetic catalogue forecasts to the more widely-used grid-based approach of previous forecast testing experiments. The simulated-catalogue approach uses the full model posteriors to create Bayesian earthquake forecasts. We show that simulated-catalogue based forecasts perform better than the grid-based equivalents due to (a) their ability to capture more uncertainty in the model components and (b) the associated relaxation of the Poisson assumption in testing. We demonstrate that the inlabru models perform well overall over various time periods, and hence that independent data such as fault slip rates can improve forecasting power on the time scales examined. Together, these findings represent a significant improvement in earthquake forecasting is possible, though this has yet to be tested and proven in true prospective mode.

scholarly journals A Comparison Between New Modification of ANWK and Classical ANWK Methods in Nonparametric Regression

2021 ◽  
Vol 5 (2) ◽  
pp. 32-37
Author(s):  
Hazhar T. A. Blbas ◽  
Wasfi T. Kahwachi
Keyword(s):  
Standard Deviation ◽  
Kernel Estimator ◽  
Median Absolute Deviation ◽  
Interquartile Range ◽  
Kernel Estimators ◽  
Statistical Research ◽  
Absolute Deviation ◽  
Research Fields ◽  
Kernel Estimations ◽  
Nonparametric Kernel

Nonparametric kernel estimators are mostly used in a variety of statistical research fields. Nadaraya-Watson kernel estimator (NWK) is one of the most important nonparametric kernel estimator that is often used in regression models with a fixed bandwidth. In this article, we consider the four new Proposed Adaptive Nadaraya-Watson Kernel Regression Estimators (Interquartile Range, Standard Deviation, Mean Absolute Devotion, and Median Absolute Deviation) rather than (Fixed Bandwidth, Adaptive Geometric, Adaptive Mean, Adaptive Range, and Adaptive Median). The outcomes in both simulation and actual data in Leukemia Cancer show that the four new ANW Kernel Estimators (Interquartile Range, Standard Deviation, Mean Absolute devotion, and Median Absolute Deviation) is more effective than the kernel estimations with fixed bandwidth in previous studies using Mean Square Error (MSE) Criterion.

scholarly journals Interpolation of Missing Precipitation Data Using Kernel Estimations for Hydrologic Modeling

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Hyojin Lee ◽  
Kwangmin Kang
Keyword(s):  
Hydrologic Modeling ◽  
Assessment Tool ◽  
Hydrologic Model ◽  
Kernel Functions ◽  
Precipitation Data ◽  
Important Research Topic ◽  
Daily Precipitation Data ◽  
Data Assessment ◽  
Regression Approach ◽  
Kernel Estimations

Precipitation is the main factor that drives hydrologic modeling; therefore, missing precipitation data can cause malfunctions in hydrologic modeling. Although interpolation of missing precipitation data is recognized as an important research topic, only a few methods follow a regression approach. In this study, daily precipitation data were interpolated using five different kernel functions, namely, Epanechnikov, Quartic, Triweight, Tricube, and Cosine, to estimate missing precipitation data. This study also presents an assessment that compares estimation of missing precipitation data throughKth nearest neighborhood (KNN) regression to the five different kernel estimations and their performance in simulating streamflow using the Soil Water Assessment Tool (SWAT) hydrologic model. The results show that the kernel approaches provide higher quality interpolation of precipitation data compared with theKNN regression approach, in terms of both statistical data assessment and hydrologic modeling performance.

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